The present disclosure relates generally to energy management, and more particularly, to a temperature adjustment method and apparatus using energy management.
The Internet, which is a human centered connectivity network where humans generate and consume information, is now evolving to the Internet of Things (IoT) where distributed entities, such as things, exchange and process information without human intervention. The Internet of Everything (IoE), which is a combination of the IoT technology and the Big Data processing technology through connection with a cloud server, has emerged. As technology elements, such as “sensing technology”, “wired/wireless communication and network infrastructure”, “service interface technology”, and “Security technology” have been demanded for IoT implementation, a sensor network, a Machine-to-Machine (M2M) communication, Machine Type Communication (MTC), and so forth have been recently researched.
Such an IoT environment may provide intelligent Internet technology services that create a new value to human life by collecting and analyzing data generated among connected things. IoT may be applied to a variety of fields including smart home, smart building, smart city, smart car or connected cars, smart grid, health care, smart appliances and advanced medical services through convergence and combination between existing Information Technology (IT) and various industrial applications.
Meanwhile, as to a method in which a conventional energy management device schedules charging/discharging power quantities of an Energy Storage System (ESS) battery in relation to a variable power rate, the ESS is charged in a light load rate time zone, and is collectively discharged in a high load rate time zone. Accordingly, at the time of linking to a specific device, such as a Heating, Ventilation, and Air Conditioning (HVAC) system, a load prediction is difficult to make, so that optimal utilization of the storage capacity of the ESS battery is difficult. Conventionally, the power quantity that is used during the high load time cannot be predicted in advance, and thereby the rate reduction effect using the charging/discharging of the ESS cannot be optimized.
That is, even in a case in which the ESS is charged during the light load rate time zone, external electrical power is used when excessively using the charging capacity of the ESS during the high load time, which leads to the generation of the excess rate. In addition, the residual quantity is generated when insufficiently using the charging capacity of the ESS during the high load time, which leads to a reduction in the Return On Investment (ROI).